Sr. Data Scientist

Transact Campus•Limerick, PA

18h•Hybrid

About The Position

The Opportunity Transact Campus is transforming the student experience through credential-driven privileges, innovative commerce solutions and payments. Our enterprise-class cloud platforms power campus life for millions of students and institutions. Join as a founding member of our new Predictive Analytics team, where you'll work with Product teams to define our ML/AI development strategy and build our machine learning infrastructure from the ground up. This is a hands-on, high-autonomy role — you'll take full ownership of the model lifecycle, from data exploration through to deployment and monitoring in production, working directly on our Databricks on Azure architecture. You'll solve real-world commerce and privileges problems — including order wait time prediction, market basket analysis, and conversational AI — while establishing the engineering standards and architectural decisions that will define how we build ML capabilities going forward. Your work will have direct, visible impact on our product and on the student experience for millions of users. Location : Limerick City - Hybrid working 1 day per week / month on site (depending on location) Employment Type : full time Experience Leve l : Senior

Requirements

5+ years of experience taking ML models from development to production in a commercial environment, with a broader background in data science or engineering
Deep, hands-on experience with Azure ML and MLflow for experiment tracking, model registry, and model serving
Strong proficiency in Python and SQL for data manipulation and analysis
Proven experience deploying and monitoring ML models in production independently, without dedicated MLOps support
Experience with ML frameworks - scikit-learn, MLFlow, TensorFlow, PyTorch, and Pandas
Experience with big data platforms (Databricks, Apache Spark) and cloud services (Azure Lakehouse, AWS, or GCP).
Solid knowledge of ML Ops principles - CI/CD for ML, model versioning, drift monitoring, and pipeline automation
Exposure to data visualisation tools (Power BI, Tableau, Looker).
Strong knowledge of statistical modelling and the ability to select and justify appropriate approaches for real-world problems
Strong communication skills - ability to present complex technical concepts clearly to non- technical stakeholders and influence product decisions with data

Nice To Haves

Master’s in Data Science, Computer Science or a related field
Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or similar, and familiarity with RAG pipeline design
Experience with Databricks for ML workloads, including MLflow and Unity Catalog
Experience in a commerce, fintech, or transactional data environment
Familiarity with Apache Spark for large-scale data processing
Experience working in Agile/Scrum environments

Responsibilities

Own the full ML lifecycle end-to-end — from data gathering, feature engineering, and model development through to deployment, serving, and production monitoring — without reliance on a dedicated MLOps function
Design, build, and deploy ML models on Databricks, leveraging MLflow for experiment tracking, model registry,
Develop solutions for privileges and commerce-focused use cases including order wait time prediction, market basket analysis, and demand forecasting
Work with the Data Architect and Data Engineering team to design and build conversational AI and chatbot capabilities, leveraging LLMs and retrieval-augmented generation (RAG) pipelines
Collaborate with the Data Analytics team to leverage existing Power BI and Databricks data infrastructure, and extend it with predictive capabilities
Define and implement MLOps best practices, CI/CD pipelines for models, and data governance standards — establishing the foundations the team will scale on
Ensure data quality, security compliance, and model reliability in production
Provide technical leadership and mentor AI/ML team members across Data Analytics and Predictive Analytics teams
Partner with cross-functional teams to find data-driven opportunities and translate them into shipped ML features
Ensure data quality, governance, and model reliability in production
Stay updated on emerging technologies in AI, ML, and data science to drive innovation
Provide technical leadership and help set the standard for ML engineering rigour as the team grows